{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. \n",
    "One idea along these lines is batch normalization which was proposed by [1] in 2015.\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [1] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n",
    "\n",
    "def print_mean_std(x,axis=0):\n",
    "    print('  means: ', x.mean(axis=axis))\n",
    "    print('  stds:  ', x.std(axis=axis))\n",
    "    print() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n",
    "\n",
    "Referencing the paper linked to above in [1] may be helpful!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:   [27.18502186 34.21455511 37.68611762]\n",
      "\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  means:  [ 1.33226763e-17 -3.94129174e-17  3.29597460e-17]\n",
      "  stds:   [0.99999999 1.         1.        ]\n",
      "\n",
      "After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n",
      "  means:  [11. 12. 13.]\n",
      "  stds:   [0.99999999 1.99999999 2.99999999]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print_mean_std(a,axis=0)\n",
    "\n",
    "gamma = np.ones((D3,))\n",
    "beta = np.zeros((D3,))\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)\n",
    "\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:   [1.01531428 1.01238373 0.97819988]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.7029261167605239e-09\n",
      "dgamma error:  7.420414216247087e-13\n",
      "dbeta error:  2.8795057655839487e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "#You should expect to see relative errors between 1e-13 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too!  \n",
    "\n",
    "In the forward pass, given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n",
    "\n",
    "we first calculate the mean $\\mu$ and variance $v$.\n",
    "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma$  and normalized data $Y$.\n",
    "The equations and graph illustration below describe the computation ($y_i$ is the i-th element of the vector $Y$).\n",
    "\n",
    "\\begin{align}\n",
    "& \\mu=\\frac{1}{N}\\sum_{k=1}^N x_k  &  v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2 \\\\\n",
    "& \\sigma=\\sqrt{v+\\epsilon}         &  y_i=\\frac{x_i-\\mu}{\\sigma}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"notebook_images/batchnorm_graph.png\" width=691 height=202>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "The meat of our problem during backpropagation is to compute $\\frac{\\partial L}{\\partial X}$, given the upstream gradient we receive, $\\frac{\\partial L}{\\partial Y}.$ To do this, recall the chain rule in calculus gives us $\\frac{\\partial L}{\\partial X} = \\frac{\\partial L}{\\partial Y} \\cdot \\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "The unknown/hart part is $\\frac{\\partial Y}{\\partial X}$. We can find this by first deriving step-by-step our local gradients at \n",
    "$\\frac{\\partial v}{\\partial X}$, $\\frac{\\partial \\mu}{\\partial X}$,\n",
    "$\\frac{\\partial \\sigma}{\\partial v}$, \n",
    "$\\frac{\\partial Y}{\\partial \\sigma}$, and $\\frac{\\partial Y}{\\partial \\mu}$,\n",
    "and then use the chain rule to compose these gradients (which appear in the form of vectors!) appropriately to compute $\\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "If it's challenging to directly reason about the gradients over $X$ and $Y$ which require matrix multiplication, try reasoning about the gradients in terms of individual elements $x_i$ and $y_i$ first: in that case, you will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. \n",
    "\n",
    "You should make sure each of the intermediary gradient derivations are all as simplified as possible, for ease of implementation. \n",
    "\n",
    "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  0.0\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 1.00x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.2611955101340957\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 2.85e-06\n",
      "W3 relative error: 3.92e-10\n",
      "b1 relative error: 2.22e-03\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 4.78e-11\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.89e-09\n",
      "gamma1 relative error: 7.47e-09\n",
      "gamma2 relative error: 2.41e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.996533220108303\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.28e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 5.55e-09\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 2.23e-10\n",
      "beta1 relative error: 6.32e-09\n",
      "beta2 relative error: 5.69e-09\n",
      "gamma1 relative error: 5.94e-09\n",
      "gamma2 relative error: 4.14e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "# You should expect losses between 1e-4~1e-10 for W, \n",
    "# losses between 1e-08~1e-10 for b,\n",
    "# and losses between 1e-08~1e-09 for beta and gammas.\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            normalization='batchnorm')\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver with batch norm:\n",
      "(Iteration 1 / 200) loss: 2.340974\n",
      "(Epoch 0 / 10) train acc: 0.107000; val_acc: 0.115000\n",
      "(Epoch 1 / 10) train acc: 0.313000; val_acc: 0.266000\n",
      "(Iteration 21 / 200) loss: 2.039365\n",
      "(Epoch 2 / 10) train acc: 0.384000; val_acc: 0.279000\n",
      "(Iteration 41 / 200) loss: 2.041103\n",
      "(Epoch 3 / 10) train acc: 0.494000; val_acc: 0.307000\n",
      "(Iteration 61 / 200) loss: 1.753903\n",
      "(Epoch 4 / 10) train acc: 0.534000; val_acc: 0.308000\n",
      "(Iteration 81 / 200) loss: 1.246585\n",
      "(Epoch 5 / 10) train acc: 0.574000; val_acc: 0.314000\n",
      "(Iteration 101 / 200) loss: 1.320590\n",
      "(Epoch 6 / 10) train acc: 0.635000; val_acc: 0.337000\n",
      "(Iteration 121 / 200) loss: 1.157329\n",
      "(Epoch 7 / 10) train acc: 0.684000; val_acc: 0.328000\n",
      "(Iteration 141 / 200) loss: 1.141054\n",
      "(Epoch 8 / 10) train acc: 0.775000; val_acc: 0.337000\n",
      "(Iteration 161 / 200) loss: 0.700218\n",
      "(Epoch 9 / 10) train acc: 0.808000; val_acc: 0.327000\n",
      "(Iteration 181 / 200) loss: 0.887479\n",
      "(Epoch 10 / 10) train acc: 0.789000; val_acc: 0.320000\n",
      "\n",
      "Solver without batch norm:\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.129000; val_acc: 0.131000\n",
      "(Epoch 1 / 10) train acc: 0.283000; val_acc: 0.250000\n",
      "(Iteration 21 / 200) loss: 2.041970\n",
      "(Epoch 2 / 10) train acc: 0.316000; val_acc: 0.277000\n",
      "(Iteration 41 / 200) loss: 1.900473\n",
      "(Epoch 3 / 10) train acc: 0.373000; val_acc: 0.282000\n",
      "(Iteration 61 / 200) loss: 1.713157\n",
      "(Epoch 4 / 10) train acc: 0.390000; val_acc: 0.310000\n",
      "(Iteration 81 / 200) loss: 1.662213\n",
      "(Epoch 5 / 10) train acc: 0.431000; val_acc: 0.298000\n",
      "(Iteration 101 / 200) loss: 1.703394\n",
      "(Epoch 6 / 10) train acc: 0.525000; val_acc: 0.346000\n",
      "(Iteration 121 / 200) loss: 1.558098\n",
      "(Epoch 7 / 10) train acc: 0.548000; val_acc: 0.321000\n",
      "(Iteration 141 / 200) loss: 1.434444\n",
      "(Epoch 8 / 10) train acc: 0.627000; val_acc: 0.331000\n",
      "(Iteration 161 / 200) loss: 1.054038\n",
      "(Epoch 9 / 10) train acc: 0.616000; val_acc: 0.316000\n",
      "(Iteration 181 / 200) loss: 0.977785\n",
      "(Epoch 10 / 10) train acc: 0.715000; val_acc: 0.329000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "print('Solver with batch norm:')\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True,print_every=20)\n",
    "bn_solver.train()\n",
    "\n",
    "print('\\nSolver without batch norm:')\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n",
    "    \"\"\"utility function for plotting training history\"\"\"\n",
    "    plt.title(title)\n",
    "    plt.xlabel(label)\n",
    "    bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n",
    "    bl_plot = plot_fn(baseline)\n",
    "    num_bn = len(bn_plots)\n",
    "    for i in range(num_bn):\n",
    "        label='with_norm'\n",
    "        if labels is not None:\n",
    "            label += str(labels[i])\n",
    "        plt.plot(bn_plots[i], bn_marker, label=label)\n",
    "    label='baseline'\n",
    "    if labels is not None:\n",
    "        label += str(labels[0])\n",
    "    plt.plot(bl_plot, bl_marker, label=label)\n",
    "    plt.legend(loc='lower center', ncol=num_bn+1) \n",
    "\n",
    "    \n",
    "plt.subplot(3, 1, 1)\n",
    "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n",
    "                      lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n",
    "plt.subplot(3, 1, 2)\n",
    "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "plt.subplot(3, 1, 3)\n",
    "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers_ws = {}\n",
    "solvers_ws = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "  print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "  bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "  model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "  bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  bn_solver.train()\n",
    "  bn_solvers_ws[weight_scale] = bn_solver\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  solver.train()\n",
    "  solvers_ws[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 1:\n",
    "Describe the results of this experiment. How does the scale of weight initialization affect models with/without batch normalization differently, and why?\n",
    "\n",
    "## Answer:\n",
    "When use batch normalization,even if we use a bad weight initialization scale,we can still get a not bad result,but without batch normalization,we are very sensetive to weight initialization scale. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and batch size\n",
    "We will now run a small experiment to study the interaction of batch normalization and batch size.\n",
    "\n",
    "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    }
   ],
   "source": [
    "def run_batchsize_experiments(normalization_mode):\n",
    "    np.random.seed(231)\n",
    "    # Try training a very deep net with batchnorm\n",
    "    hidden_dims = [100, 100, 100, 100, 100]\n",
    "    num_train = 1000\n",
    "    small_data = {\n",
    "      'X_train': data['X_train'][:num_train],\n",
    "      'y_train': data['y_train'][:num_train],\n",
    "      'X_val': data['X_val'],\n",
    "      'y_val': data['y_val'],\n",
    "    }\n",
    "    n_epochs=10\n",
    "    weight_scale = 2e-2\n",
    "    batch_sizes = [5,10,50]\n",
    "    lr = 10**(-3.5)\n",
    "    solver_bsize = batch_sizes[0]\n",
    "\n",
    "    print('No normalization: batch size = ',solver_bsize)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "    solver = Solver(model, small_data,\n",
    "                    num_epochs=n_epochs, batch_size=solver_bsize,\n",
    "                    update_rule='adam',\n",
    "                    optim_config={\n",
    "                      'learning_rate': lr,\n",
    "                    },\n",
    "                    verbose=False)\n",
    "    solver.train()\n",
    "    \n",
    "    bn_solvers = []\n",
    "    for i in range(len(batch_sizes)):\n",
    "        b_size=batch_sizes[i]\n",
    "        print('Normalization: batch size = ',b_size)\n",
    "        bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n",
    "        bn_solver = Solver(bn_model, small_data,\n",
    "                        num_epochs=n_epochs, batch_size=b_size,\n",
    "                        update_rule='adam',\n",
    "                        optim_config={\n",
    "                          'learning_rate': lr,\n",
    "                        },\n",
    "                        verbose=False)\n",
    "        bn_solver.train()\n",
    "        bn_solvers.append(bn_solver)\n",
    "        \n",
    "    return bn_solvers, solver, batch_sizes\n",
    "\n",
    "batch_sizes = [5,10,50]\n",
    "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 2:\n",
    "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n",
    "\n",
    "## Answer:\n",
    "If we use batch normalization we should use a bigger batch size,if that we can get a more accurate mean and variance.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization\n",
    "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n",
    "\n",
    "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [2]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n",
    "\n",
    "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 3:\n",
    "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n",
    "\n",
    "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n",
    "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1.  \n",
    "3. Subtracting the mean image of the dataset from each image in the dataset.\n",
    "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n",
    "\n",
    "## Answer:\n",
    "batch normalization 3 \n",
    "layer normalization 124\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization: Implementation\n",
    "\n",
    "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n",
    "\n",
    "Here's what you need to do:\n",
    "\n",
    "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the cell below to check your results.\n",
    "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the second cell below to check your results.\n",
    "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n",
    "\n",
    "Run the third cell below to run the batch size experiment on layer normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before layer normalization:\n",
      "  means:  [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n",
      "  stds:   [10.07429373 28.39478981 35.28360729  4.01831507]\n",
      "\n",
      "After layer normalization (gamma=1, beta=0)\n",
      "  means:  [ 4.81096644e-16  0.00000000e+00  0.00000000e+00 -2.96059473e-16]\n",
      "  stds:   [0.99999995 0.99999999 1.         0.99999969]\n",
      "\n",
      "After layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n",
      "  means:  [5. 5. 5. 5.]\n",
      "  stds:   [2.99999985 2.99999998 2.99999999 2.99999907]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after layer normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 =4, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before layer normalization:')\n",
    "print_mean_std(a,axis=1)\n",
    "\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After layer normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)\n",
    "\n",
    "gamma = np.asarray([3.0,3.0,3.0])\n",
    "beta = np.asarray([5.0,5.0,5.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.433615657860454e-09\n",
      "dgamma error:  4.519489546032799e-12\n",
      "dbeta error:  2.276445013433725e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "ln_param = {}\n",
    "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n",
    "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n",
    "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n",
    "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n",
    "\n",
    "#You should expect to see relative errors between 1e-12 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization and batch size\n",
    "\n",
    "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 4:\n",
    "When is layer normalization likely to not work well, and why?\n",
    "\n",
    "1. Using it in a very deep network\n",
    "2. Having a very small dimension of features\n",
    "3. Having a high regularization term\n",
    "\n",
    "\n",
    "## Answer:\n",
    "2 If use a small dimension,the mean and varience of the dimension will became random ,can't show it's common ground.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
